On a methodological level, statistical models are generally seen as useful and mainly tested for their faithfulness. Statistical metrics which rudimentary consider usefulness are information criteria such as the AIC [1]} or BIC [2]}. Statistical tests also evaluate faithfulness through confidence and significance statements, which are largely ignored in the evaluation of queries in IML. In addition, statistical models often incorporate assumptions about the distribution of the estimated parameters, which also serve as the query part of the model, and rely, e.g., on the law of large numbers or independence assumptions. These assumptions have to be tested and limit the expressiveness of an explanation. For example, coefficients in a linear regression model estimated via least squares can at best be unbiased and make conclusions about linear relationships (Gauß-Markov Theorem) [3]}. However, in comparison, IML methods are also often based on axioms which could be compared to such assumptions.
